Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 ā basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 ā daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 ā you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 ā exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Software Development @ 3
ElasticSearch @ 3
Java @ 3
NoSQL @ 3
Algorithms @ 3
Distributed Systems @ 3
JVM @ 3
Machine Learning @ 3
Data Science @ 3
Communication @ 3
Mathematics @ 6
MongoDB @ 3
Solr @ 3
Debugging @ 3
CUDA @ 3
GPU @ 3
AI @ 3
HPC @ 3
Performance Analysis @ 3
- 1-2 ā basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 ā daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 ā you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 ā exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is seeking Java engineering interns to work on cuVS, an open-source suite of libraries for unstructured data processing and vector search algorithms on GPUs. cuVS uses NVIDIA CUDA for low-level compute optimization and exposes high-performance GPU compute through user-friendly languages such as Java. The cuVS team builds building blocks to accelerate Java-based libraries (e.g., Lucene, JVector) used in databases like OpenSearch, Solr, MongoDB, and Elasticsearch.
Responsibilities
- Analyze, design, and implement optimized GPU algorithms for large-scale vector search, databases, and machine learning.
- Expand and improve integration of NVIDIA cuVS into high-level vector search libraries and vector databases.
- Perform performance analysis, benchmarking, and troubleshooting of associated libraries.
- Develop, benchmark, and explore tuned custom solutions for accelerating vector preprocessing, clustering, indexing, and search, including disk-based indexing and scalable architectural improvements.
Requirements
- Currently enrolled in a Masters or PhD program in Data Science, Machine Learning, or Computer Science.
- Strong analytical problem-solving skills; solid algorithms and mathematics fundamentals.
- Excellent software development skills: programming, debugging, performance analysis, and test design, especially within the Java ecosystem and the JVM.
- Experience with NoSQL / search-related technologies: Lucene, Elasticsearch, OpenSearch, MongoDB, Solr.
- Good communication and documentation habits.
Ways to stand out
- Experience developing distributed algorithms and running on distributed systems (HPC, Cloud).
- Distributed system development experience.
- Experience debugging multi-language and multi-hardware systems.
- Experience with vector databases such as Milvus, Pinecone, LanceDB.
- Familiarity with nearest neighbor algorithms (graph-based and inverted file indexes).
- Familiarity with machine learning concepts like clustering and dimensionality reduction.
- GPU programming knowledge (CUDA/C++) is a plus; the team is willing to teach GPU programming if you lack it.
Compensation & Benefits
- Internship hourly rate: 20 USD - 71 USD.
- Eligible for NVIDIA intern benefits (link provided in original posting).
Other details
- Applications accepted at least until July 3, 2026.
- This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.